Deep MultiModal Learning for Automotive Applications

Research Project, 2023 – 2027

Purpose and goal
This project aims to create multimodal sensor fusion methods for advanced and robust automotive perception systems. The project will focus on three key areas: (1) Develop multimodal fusion architectures and representations for both dynamic and static objects. (2) Investigate self-supervised learning techniques for the multimodal data in an automotive setting. (3) Improve the perception system’s ability to robustly handle rare events, objects, and road users.

Expected results and effects
In this project we are focusing on techniques that can improve the accuracy and robustness of perception systems of Autonomous Drive (AD) and Advanced Driver Assistance Systems (ADAS). Therefore, we expect that our techniques contribute to enhanced safety of ADAS/AD equipped vehicles which in turn can accelerate the public adoption of AD systems. Through this increased public adoption, we hope to contribute to a considerably safer transportation for all road users.